Which sampling method tends to have the greatest degree of sampling error?

• A. Simple random sample
• B. Systematic sample
• C. Stratified random sample
• D. Cluster sample

Answer: (D)

Sampling error is driven by how representative the selected units are of the whole population. Cluster sampling tends to have the greatest error because units within the same cluster are usually more alike than units from different clusters. When you sample entire groups, you’re not picking independent observations from the whole population; the similarities inside clusters inflate the variance, lowering the effective sample size—the design effect grows with larger cluster size and higher intracluster correlation. That extra variation makes estimates less precise compared with other methods.

In contrast, simple random sampling gives each unit an equal chance and tends to produce the most precise estimate for a given total sample size. Stratified random sampling reduces variability by ensuring representation from key subgroups, which typically lowers sampling error. Systematic sampling can perform similarly to simple random sampling if there’s no hidden order or pattern that aligns with the sampling interval; it can be biased if such a pattern exists.